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Research Papers

A General Empirical Energy Consumption Model for Computer Numerical Control Milling Machine

[+] Author and Article Information
Yadan Zeng

Department of Mechanical Engineering,
University of Wisconsin Milwaukee,
3200 N Cramer St, EMS 506,
Milwaukee, WI 53211
e-mail: zengy@uwm.edu

Tonghui Li

Department of Mechanical Engineering,
University of Wisconsin Milwaukee,
3200 N Cramer St, EMS 506,
Milwaukee, WI 53211
e-mail: tonghui@uwm.edu

Yelin Deng

Department of Mechanical Engineering,
University of Wisconsin Milwaukee,
3200 N Cramer St, EMS 506,
Milwaukee, WI 53211
e-mail: deng5@uwm.edu

Chris Yuan

Mem. ASME
Department of Mechanical and
Aerospace Engineering,
Case Western Reserve University,
10900 Euclid Ave,
Cleveland, OH 44106
e-mail: chris.yuan@case.edu

1Corresponding author.

Manuscript received April 16, 2018; final manuscript received December 14, 2018; published online January 4, 2019. Assoc. Editor: Karl R. Haapala.

J. Manuf. Sci. Eng 141(2), 021020 (Jan 04, 2019) (7 pages) Paper No: MANU-18-1244; doi: 10.1115/1.4042306 History: Received April 16, 2018; Revised December 14, 2018

Energy consumption of computer numerical control (CNC) machines is significant and various empirical models have been developed to model the specific energy consumption (SEC) of CNC machines. However, most of the models are developed for specific machines and hence have limited applications in manufacturing industry. In this research, a general empirical SEC model for milling machine at certain power level is developed based on actual cutting experimental data. In this model, stand-by power and spindle power are used in the SEC model for the first time. The material removal rate (MRR) is used to represent the cutting parameter. The proposed model is fitted by the regression analysis and validated using experimental data. Results show that the proposed model can be applied on various milling machines with an average absolute residual ratio of 6%. The model is also validated through a series of cutting experiments on a machine center, with an accuracy of 91.5%, for the SEC calculation.

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References

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Figures

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Fig. 1

Power profile of a 5.5-kW machine center (Sharp SV2414-SE)

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Fig. 2

Specific energy consumption trend for machine tools [7,11,18,20] with 11-kW spindle motor at different MRRs

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Fig. 3

Curve fitting results from matlab

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Fig. 4

Comparison of residual ratios for selected machines

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Fig. 5

Comparison of calculated and experimental SEC results

Tables

Errata

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